Cooperative Sensing Scheduling for Energy-Efficient Cognitive Radio Networks

Zhang, Tengyi; Tsang, D.H.K.

doi:10.1109/tvt.2014.2346539

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…This is recently re-validated by [90] where authors use discrete-convexity tools to maximize the achievable throughput in both homogeneous and heterogeneous scenarios. Another shortcoming of the works in [87]- [90] is assumption of the perfect reporting environment.…”

Section: Related Workmentioning

confidence: 91%

“…Even though all these previous studies have important contributions, they lack for generality of exploitation di↵erent voting rules and energy e ciency by assuming AND/OR voting rule for the sake of tractability since the majority voting rule is already pointed out to be the most energy e cient voting rule in the context of additional SNR requirement [13]. This is recently re-validated by [90] where authors use discrete-convexity tools to maximize the achievable throughput in both homogeneous and heterogeneous scenarios. Another shortcoming of the works in [87]- [90] is assumption of the perfect reporting environment.…”

Section: Related Workmentioning

confidence: 99%

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Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

Çelik

Kamal

2016

IEEE Trans. Cogn. Commun. Netw.

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Section: Related Workmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

Çelik

Kamal

2016

IEEE Trans. Cogn. Commun. Netw.

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“…Meanwhile, energy efficiency (EE) is also a key metric for 5G, in which energy consumption needs to be reduced on the order of several magnitudes [6]. Note that extra power consumption incurred by spectrum sensing makes the power saving issue more critical in CR systems [7, 8]. Until now, large amounts of researches have been conducted to study energy-efficient transmission in OFDM-based CR systems.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Layered Video Multicast over OFDM-Based Cognitive Radio Systems

Xu¹,

Li²,

Xu³

et al. 2015

International Journal of Distributed Sensor Networks

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An energy-efficient layered video multicast (LVM) scheme for “bandwidth-hungry” video services is studied in OFDM-based cognitive radio (CR) systems, where the video data is encoded into a base layer and several enhancement layers with the former intended for all subscribers to guarantee the basic quality of reconstructed video and the latter aiming at the quality improvement for the promising users with good channel conditions. Moreover, in order to balance user experience maximization and power consumption minimization, a novel performance metricenergy utility(EU) is proposed to measure the sum achieved quality of reconstructed video at all subscribers when unit transmit power is consumed. Our objective is to maximize the system EU by jointly optimizing the intersession/interlayer subcarrier assignment and subsequent power allocation. For this purpose, we first perform subcarrier assignment for base layer and enhancement layers using greedy algorithm and then present an optimal power allocation algorithm to maximize the achievable EU using fractional programming. Simulation results show that the proposed algorithms can adaptively capture the state variations of licensed spectrum and dynamically adjust the video transmission to exploit the scarce spectrum and energy resources adequately. Meanwhile, the system EU obtained in our algorithms is greatly improved over traditional spectrum efficiency (SE) and energy efficiency (EE) optimization models.

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“…There are two main methods to reduce the cooperative sensing overhead: the selective reporting and the sequential detection. In [7] [8], the authors propose a scheme that cutting sensing overhead via user selection, which is only applied in perfect reporting channels. In [9], considering the errors, which are produced in the transmission process, the scheme only allows those users, which detect the presence of PU to report the results.…”

Section: Introductionmentioning

confidence: 99%

Low-overhead cooperative spectrum sensing technology for cognitive radio networks

Liu

Yang

Zhang

et al. 2014

International Conference on Cyberspace Technology (CCT 2014)

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In the past few years the cooperative sensing in cognitive radio networks (CRNs) has attracted considerable attention because of its high reliability. But a large number of cooperative users produce a huge overhead in the reporting process. In this paper, we propose a new scheme to reduce the sensing overhead in CRNs, called low-overhead cooperative sensing scheme (LCS). In this scheme, we only allow those users, which detect the presence of the PU report the sensing results. Then we use a pre-sensing sequential detection technology to decrease the whole sensing time T. Considering the preload sensing slots, we derive a formula to express the overall miss detection probability. In order to minimize the average time of spectrum sensing (AT), we design a rule to protect primary transmissions over the Rayleigh fading channel. Simulation results show that the proposed algorithm can achieve superiority and efficiency of the LCS scheme. It is also seen from the results that our algorithm can reach the given sensing accuracy for CRNs.

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Cooperative Sensing Scheduling for Energy-Efficient Cognitive Radio Networks

Cited by 14 publications

References 15 publications

Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

Energy-Efficient Layered Video Multicast over OFDM-Based Cognitive Radio Systems

Low-overhead cooperative spectrum sensing technology for cognitive radio networks

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